A Graph-Based Multi-Kernel Feature Weight Learning Framework For Detection And Grading Of Prostate Lesions Using Multi-Parametric Mr Images
PROCEEDINGS 2017 4TH IAPR ASIAN CONFERENCE ON PATTERN RECOGNITION (ACPR)(2017)
摘要
Prostate cancer is the third leading causes of death in men. However, the disease is curable if diagnosed early. During the past decades, multi-parametric magnetic resonance imaging (mpMRI) has been shown to be superior to trans-rectal ultrasound (TRUS) in detecting and localizing prostate cancer lesions to guide prostate biopsies and radiation therapies. The goal of this paper is to develop a simple and accurate graph-based regression framework for voxel-wise detection and grading of prostate cancer using mpMRIs. In the framework, groups of features were first extracted from the mpMRIs, and a graph-based multi-kernel model was proposed to learn the weights of the groups of features and the similarity matrix simultaneously. A Lapalacian regression model was then used to estimate the PIRADS score of each voxels which characterizes how likely a voxel is cancerous. Experimental results of detection and grading of prostate lesions evaluated by six metrics show that the proposed method yielded convincing results.
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关键词
Prostate Cancer Detection,Multi-parametric MRI,Graph-based algorithm,Locally Linear Embedding,Kernel tricks
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